Adaptive digital assistant and spoken genome

ABSTRACT

Embodiments of the invention include a context sensitive adaptive digital assistant for personalized interaction. Embodiments of the invention also include a spoken genome for characterization and analysis of human voice. Aspects of the invention include selecting a starter vocabulary, receiving voice communications from a user, and modifying the starter vocabulary to generate a personalized lexicon. Aspects of the invention also include analyzing and categorizing human voice according to a plurality of characteristics, and creating a spoken genome database.

DOMESTIC PRIORITY

This application is a division of and claims priority from U.S. patentapplication Ser. No. 15/497,536, filed on Apr. 26, 2017, the entirecontents of which are incorporated herein by reference.

BACKGROUND

The present invention relates generally to a digital assistant andspoken genome, and more specifically to a context sensitive adaptivedigital assistant for personalized interaction and spoken genome forcharacterization and analysis of human voice.

Personal digital assistants, including digital assistants associatedwith smart devices, are increasingly becoming integrated into the dailylives of the general population. Such personal digital assistants canreadily facilitate looking up information, scheduling appointments,setting task lists, and the like through vocal requests by the user.Conventional personal digital assistants, although increasingly popularwith smart device users, lack personalization. In addition, althoughpeople can enjoy music and the sound their own voice, they may yetresort to silent texting and internet posting for communication.Qualification of sound and voice characteristics could aid not only theconsumer, but also the provider of consumer content.

SUMMARY

In accordance with embodiments of the invention, a computer-implementedmethod for personalized digital interaction includes selecting a startervocabulary from a starter vocabulary set. The method also includesreceiving a plurality of user voice communications from a user. Themethod also includes generating a frequent word list based at least inpart on the plurality of user voice communications. The method alsoincludes modifying the starter vocabulary with a plurality of words fromthe frequent word list to generate a personalized lexicon. The methodalso includes generating a personalized verbal output based at least inpart on the personalized lexicon.

In accordance with embodiments of the invention, a computer programproduct for characterization and analysis of human voice includes acomputer readable storage medium readable by a processing circuit andstoring program instructions for execution by the processing circuit forperforming a method. The method includes receiving a plurality of mediafiles, wherein the plurality of media files include spoken words. Themethod also includes categorizing the plurality of media files accordingto spoken genome properties to create a categorized spoken genomedatabase. The method also includes receiving a user media preference.The method also includes determining a user media profile based at leastin part on the user media preference, wherein the media profile includesa spoken genome property. The method also includes generating a mediarecommendation based at least in part on the user media profile and thecategorized spoken genome database.

In accordance with embodiments of the invention, a processing system forcharacterization and analysis of human voice includes a processor incommunication with one or more types of memory. The processor isconfigured to receive a first reference voice sampling, wherein thefirst reference voice sampling includes a plurality of reference voicescorresponding to a first known reference quality. The processor is alsoconfigured to analyze the first voice sampling to determine a spokengenome property corresponding to the first known reference quality.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter of embodiments of the invention is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The foregoing and other features and advantages ofthe one or more embodiments described herein are apparent from thefollowing detailed description taken in conjunction with theaccompanying drawings in which:

FIG. 1 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 2 depicts abstraction model layers according to an embodiment ofthe present invention.

FIG. 3 depicts a computing node according to one or more embodiments ofthe present invention.

FIG. 4 depicts a diagram illustrating an exemplary adaptive digitalassistant system according to one or more embodiments of the presentinvention.

FIG. 5 depicts a flow diagram illustrating a method for personalizeddigital interaction according to one or more embodiments of the presentinvention.

FIG. 6 depicts a diagram illustrating an exemplary voicecharacterization system according to one or more embodiments of thepresent invention.

FIG. 7 depicts a flow diagram illustrating a method for characterizationand analysis of human voice according to one or more embodiments of thepresent invention.

FIG. 8 depicts a flow diagram illustrating a method for characterizationand analysis of human voice according to one or more embodiments of thepresent invention.

FIG. 9 depicts a flow diagram illustrating a method for characterizationand analysis of human voice according to one or more embodiments of thepresent invention.

DETAILED DESCRIPTION

Embodiments of the invention relate to systems and methods forcharacterization, analysis, and personalization of voice.

A large appeal of personal digital assistants is that they desirablyreduce or even eliminate the need to type information into searchengines and scheduling programs, and in some instances, they can provideshortcuts to commands associated with a smart device. Conventionalpersonal digital assistants, although increasingly popular with smartdevice users, lack personalization. Traditional personal digitalassistants have the same characteristics for each user, including, forexample, speaking with the same lexicon and same degree of politenessfor every person serviced by the personal digital assistant. This lackof personalization, however, is undesirable for a user of suchassistants. For example, a user who interacts multiple times daily witha personal digital assistants could prefer characteristics that are lessautomated to increase the quality of and frequency of interaction and toachieve a communication that feels more like a human-human interactionthan a human-machine interaction.

Embodiments of the invention include a personalized digital assistantthat can adapt its lexicon and vocal mannerisms for an individual user.In some embodiments of the invention, personal digital assistants arepersonalized to an individual based at least in part on one or morecharacteristics and can further adapt with continued use.

With increased emphasis on texting for communication and sharingthoughts and feelings through posting of text and pictures on socialmedia, the emphasis and focus on sound and the human voice hasdecreased. Although people can enjoy music and the sound their ownvoice, they may yet resort to silent texting and internet posting forcommunication. However, wording expressed by people vocally, includingaspects such as cadence, tonality, dynamics and wording, can have apotentially large impact on human emotions and actions. For example,characteristics of sound and voice can impact an individual's decisionof whether to watch a commercial or television program. Although actionsand emotions can be guided by how much people like what they hear,individuals can lack an ability to pre-determine whether whatindividuals hear will be appealing or influential.

Various aspects of sound characteristics, such as cadence, tonality,dynamics, and wording choice, can have a potentially dramatic impact onuser emotion and activity. Voice and sound characteristics can affecthow listeners perceive, appreciate, and pay attention to a speaker. Forexample, listening to a voice with a low volume and slow cadence can bemore likely to induce sleepiness and feelings of relaxation thanlistening to a voice with a high volume and fast cadence. Moreover,individual listeners or categories of listeners can have their own soundpreferences, which can be known or unknown to a given individual. Forexample, a female voice at a given pitch could be more appealing orinfluential to a 30-year old male than a 60-year old female and, thus,could be a better choice for a truck advertisement. Similarly, speakingcadence can be an important characteristic for listening ability butalso as an indicator of content of a viewable program. Voice and soundcharacteristics, if known and understood, can be used for targetedadvertising, tailored content perception, for example in educationalcontexts, and a variety of other purposes.

Embodiments of the invention include a spoken genome that categorizesvoices based at least in part on pitch, cadence, tonality, rhythm,accent, timing, elocution, and related or similar characteristics thatcan affect how listeners perceive, appreciate, or pay attention to aspeaker. Embodiments of the invention can guide listeners and contentproviders to content they will like. Embodiments of the invention canfacilitate gaining the interest, of target audiences, for example,consumers, television viewers, or students. Thus, embodiments of theinvention can provide benefits to advertisers, educators, and televisionprogram providers, who can desire to provide subtly targeted content. Insome embodiments of the invention, a user can use a spoken genome toidentify content that is likely to be of interest. For example, a fan oftelevision programming that provides characteristic rapid-fire cleverdialogue, can use the spoken genome to find other television programmingwith similar rapid-fire dialogue. In some embodiments of the invention,a spoken genome can qualify advertisements and television shows. In someembodiments of the invention, a spoken genome can guide marketers andproducers as they set out to target certain audiences.

It is understood in advance that although this description includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model can includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but can be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It can be managed by the organization or a third party andcan exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It can be managed by the organizations or a third partyand can exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure including a networkof interconnected nodes.

Referring now to FIG. 1, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N can communicate. Nodes 10 cancommunicate with one another. They can be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 1 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 2, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 1) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 2 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities can be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 can provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources can include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment can be utilized. Examples of workloads andfunctions which can be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and voice categorization and personalization96.

Referring now to FIG. 3, a schematic of a cloud computing node 100included in a distributed cloud environment or cloud service network isshown according to a non-limiting embodiment. The cloud computing node100 is only one example of a suitable cloud computing node and is notintended to suggest any limitation as to the scope of use orfunctionality of embodiments of the invention described herein.Regardless, cloud computing node 100 is capable of being implementedand/or performing any of the functionality set forth hereinabove.

In cloud computing node 100 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that can besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 can be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules can includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 can be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules can be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 3, computer system/server 12 in cloud computing node100 is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 can include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media can be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 can further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 can include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,can be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, can include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 can also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.,one or more devices that enable a user to interact with computersystem/server 12, and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Turning now to a more detailed description of embodiments of the presentinvention, FIG. 4 depicts a diagram illustrating an exemplary adaptivedigital assistant system 200. The system 200 includes a primary learninginput 202. The primary learning input 202 includes components that canbe used to establish a starter vocabulary 232. For example, the primarylearning input 202 can include any general data that can be used toselect from a defined subset of starter vocabularies. For example, theprimary learning input can include user demographics 214, such as age,gender, geographic location, marital status, ethnicity, or educationallevel. In some embodiments of the invention, the primary learning input202 can include a user selected base speech 216. For example, a user canselect their own base speech vocabulary, upon system start up, from alist of predefined starter vocabularies, such as teenage girl, middleaged woman, man aged 60 to 70, and the like. The primary learning input202 can include, in some embodiments of the invention, a specializedlibrary selection 218. For example, a user can be prompted, upon systemstart up, to optionally select a specialized library corresponding to aspecialized interest or occupation, such as a music specialization or anengineering, medical or other occupation. A specialized libraryselection 218, for instance, can aid in enhancement or supplementationof the base vocabulary, providing greater understanding of the usercommands by the personal assistant and, thereby, enhanced functionality.

In some embodiments of the invention, the system 200 includes asecondary learning input 204. The secondary learning input 204 caninclude a variety of sources of vocabulary and grammatical expressioninvolving the user or others in frequent contact with the user, such asuser friends, family, and colleagues. In some embodiments of theinvention, the system 200 can prompt a user for permission to accesssecondary sources. Secondary learning input 204 can include textmessages 220, including text messages sent by the user or text messagessent to the user. The system 200 can, for example, analyze text messagesfor frequent words or phrases. In some embodiments of the invention,text messages 220 include a subset of all text messages received by theuser, such as text messages sent by a pre-determined list of individualsor text messages from frequent senders. Secondary learning input 204 caninclude phone calls 222. For example, the system 200 could receive userpermission to access the input and output of a smartphone and canmonitor conversations for frequently used words or phrases, for voiceaccents, vocal mannerisms, and specialized vocabulary. Secondarylearning input 204 can include email messages 224, including emailmessages sent by the user or email messages sent to the user. The system200 can, for example, analyze email messages for frequent words orphrases. In some embodiments of the invention, email messages 224include a subset of all email messages received by the user, such astext messages sent by a pre-determined list of individuals or textmessages from frequent senders. Secondary learning input 204 can includesocial media 226 access. For example, the system 200 could receive userpermission to access social media postings by a user or the user'sfriends or family and can monitor posts for frequently used words orphrases, or specialized vocabulary.

In some embodiments of the invention, secondary learning input 204includes user-personal assistant interaction 228. User personalassistant interaction 228 includes all voice communications a userprovides to the personal assistant, including verbal commands andstatements, such as scheduling requests, meeting requests, researchrequests, requests to perform tasks such as writing emails, textmessages, or placing telephone calls, comments on results from thepersonal assistant, responses to personal assistant output, such asresponses to requests for clarification and follow up questions by thepersonal assistant, and the like.

In some embodiments of the invention, the system 200 includes externalclassification systems 230. For example, when the system 200 encountersan unknown word or phrase, the system 200 can consult externalclassification systems 230, such as web-based dictionaries or urbandictionaries, to determine a meaning or characterization for the word orphrase.

As is shown, the system 200 can include an adaptive personal assistantlexicon 206. The adaptive personal assistant lexicon can analyze theprimary learning input and the secondary learning input to provide apersonalized verbal output. In some embodiments of the invention, theadaptive personal assistant lexicon 206 includes a starter vocabulary232. The adaptive personal assistant lexicon 206 optionally includes aspecialized vocabulary 234. The adaptive personal assistant lexicon 206includes a personalized lexicon 236. The personalized lexicon 236 can begenerated based at least in part on the primary learning input 202, thesecondary learning input 204, and external classification systems 230.The personalized lexicon 236 can be continuously or periodicallymodified or updated, for example, through continuous or periodic receiptof secondary learning input 204. In some embodiments of the invention,the adaptive personal assistant lexicon 206 includes accent features238. Accent features 238 can include pre-defined vocabulary orpronunciation features, such as features associated with a knowngeographic region (southern United States, Australia, Canada, etc.). Insome embodiments of the invention, accent features 238 can includevocabulary or pronunciation features derived from analyzing speech fromthe user and optionally the user's friends and family.

The system 200 includes an output 212 including vocal digital outputfrom the personal assistant. The output 212 can include any outputrequested by a user of a personal digital assistant, such as responsesto request for information, such as requests or directions, schedules,internet searches, weather, and the like; responses for requests toperform tasks, such as calendaring, telephoning, sending text messagesor emails, and the like; requests for clarification; reminders ofupcoming events, etc. In some embodiments of the invention, the adaptivepersonal assistant lexicon 206 can adapt the personal assistant outputto include words and vocal mannerisms of the user. For example, thepersonal digital assistant can adapt its speech in order to approximatea relationship of a friend or trusted advisor.

FIG. 5 depicts a flow diagram illustrating a method 300 for personalizeddigital interaction according to one or more embodiments of the presentinvention. The method 300 includes, as shown at block 302, selecting astarter vocabulary based at least in part on user characteristics. Themethod 300 also includes, as shown at block 304, receiving a pluralityof user voice communications from a user to a personal assistant. Themethod 300 also includes, as shown at block 306, optionally receiving aplurality of verbal and nonverbal third-party communications between auser and a third-party. For example, the third-party communications caninclude text messages, social media posts, telephone conversations, oremails between a user and a third-party, such as a friend, colleague,acquaintance, co-worker, or family member. The method 300 also includes,as shown at block 308, generating a frequent word list based at least inpart on a plurality of user voice communications and optionalthird-party communications. The method 300 also includes, as shown atblock 310, generating a personalized vocabulary based at least in parton the starter vocabulary and frequent word list. The method 300 alsoincludes, as shown at block 312, delivering a personalized verbal outputto a user based at least in part on the personalized vocabulary.

In some embodiments of the invention, methods for personalized digitalinteraction include receiving a secondary learning input from a smartdevice and modifying the personalized vocabulary based at least in parton the secondary learning input.

In some embodiments of the invention, selecting the starter vocabularyincludes receiving a user demographic data set. The user demographicdata set can include any information helpful to determining an initialvocabulary, such as age, gender, geographic location, and the like. Insome embodiments of the invention, selecting the starter vocabularyincludes comparing a user demographic data set to a plurality ofcharacteristics of a starter vocabulary set. The starter vocabulary setcan include base vocabularies of designated age groups, genders, and thelike. Selecting the starter vocabulary can also include determiningbased at least in part on the comparison, a preferred startervocabulary.

In some embodiments of the invention, methods for personalized digitalinteraction include analyzing a voice command from a user to determinethe user's mood and adjusting a characteristic of the personalizedverbal output based at least in part on the mood. For example, a moodfilter can analyze the timbre, tenor, and inflection of words andidentify the mood of the speaker. The adaptive digital assistant canoptionally modify the verbal output delivery to better match the mood ofthe user. For instance, if a speaker is very dynamic and loud andlaughing, the adaptive digital assistant can be dynamic and funny. If aspeaker is sober and speaks slowly with little inflection or dynamics,the adaptive digital assistant can provide a relatively muted deliveryoutput. In some embodiments of the invention, a user can set mood filterpreferences, for example a user can set the mood filter to operate onlyupon request or during specified time intervals. In some embodiments ofthe invention, methods for personalized digital interaction includeselecting a set of accent features and adjusting a characteristic of thepersonalized verbal output based at least in part on the accentfeatures.

Embodiments of the invention provide a voice characterization system.Voice characterization systems and methods according to embodiments ofthe invention can aid advertisers, television producers, and otherproviders of spoken content in reaching an appealing to targetaudiences.

Characterization of music, such as in the case of the music genomeproject, can include analysis, characterization, and grouping of songs.Characterization of speech, on the other hand, presents a number ofcomplexities not present in the case of music. For example, in speech,pitch variation can form part of a more complex set of modulations knownas prosody. Prosody includes speech parameters that can apply on avariety of levels, including the level of a syllable, word, phrase, orsentence, and moreover, can involve distinguishing word meanings in tonelanguages, distinguishing questions from statements, signaling emotion,such as irony and sarcasm, and other similar nuances of speech. Suchcharacteristics and nuances, however, can play a role in a listener'soverall preference or dislike for an instance of spoken word.

FIG. 6 depicts a diagram illustrating an exemplary voicecharacterization system 400 according to one or more embodiments of thepresent invention. The system 400 can include a reference voice sampling402. The system 400 can also include a spoken genome hub 404. The spokengenome hub 404 can include a voice analysis system 406 and a voicecharacteristic database 408. In some embodiments of the invention, thesystem 400 also includes a target voice input 410. Target voice input410 can include a voice input that is desired to be analyzed or comparedto reference voice samplings or characteristics in the voicecharacteristic database 408. In some embodiments of the invention, thesystem 400 includes an output display 412.

Reference voice sampling 402 can include a plurality of voice data filesassociated with a variety of known characteristics. For example, thevoice sampling 402 can include a plurality of voices having differentgenders, ages, socio-economic characteristics, educational levels, orany other quality that could be useful for categorization in a consumeror media context, an advertising context, an educational context, andthe like.

Spoken genome hub 404 can include a voice analysis system 406 that cananalyze human voice and categorize voices based at least in part ongender, pitch, cadence, tonality, rhythm, accent, timing, slurring,elocution, and other similar or related characteristics that can affecthow a listener perceives, appreciates, or pays attention to a speaker.Spoken genome hub 404 can include a voice characterization database 408that can include a plurality of reference qualities and associated voicecharacteristics.

FIG. 7 depicts a flow diagram illustrating a method for characterizationand analysis of human voice 500 according to one or more embodiments ofthe present invention. The method 500 includes, as shown at block 502,receiving a plurality of media containing spoken words. The method 500also includes, as shown at block 504, categorizing the plurality ofmedia according to spoken genome properties to create a categorizedspoken genome database. The method 500 also includes, as shown at block506, receiving a user media preference. The method 500 also includes, asshown at block 508, determining a user media profile based at least inpart on a user media preference, wherein the user media profile includesa preferred spoken genome property. The method 500 also includes, asshown at block 510, providing a media recommendation based at least inpart on the user media profile and the categorized spoken genomedatabase.

FIG. 8 depicts a flow diagram illustrating another method forcharacterization and analysis of human voice 600 according to one ormore embodiments of the present invention. The method 600 includes, asshown at block 602, receiving a first voice sampling including referencevoices corresponding to a first known reference quality. The method 600also includes, as shown at block 604, analyzing the first voice samplingto determine a spoken genome property corresponding the first knownreference quality. The method 600 also includes, as shown at block 606,receiving a second voice sampling including reference voicescorresponding to a second known reference quality. The method 600 alsoincludes, as shown at block 608, analyzing the second voice sampling todetermine spoken genome properties corresponding to the second knownreference quality. The method 600 also includes, as shown at block 610,generating a spoken genome database.

FIG. 7 depicts a flow diagram illustrating yet another method forcharacterization and analysis of human voice 700 according to one ormore embodiments of the present invention. The method 700 includes, asshown at block 702, receiving a voice sampling including referencevoices corresponding to a known reference quality. The method 700 alsoincludes, as shown at block 704, analyzing the voice sampling todetermine spoken genome properties corresponding to known referencequalities. The method 700 also includes, as shown at block 706,receiving a target voice input corresponding to a candidate. Thecandidate can be, for example, an advertising candidate. The method 700also includes, as shown at block 708, determining a spoken genomeproperty corresponding to a target voice input. The method 700 alsoincludes, as shown at block 710, comparing a target voice spoken genomeproperty to the spoken genome property corresponding to the knownreference quality. The method 700 also includes, as shown at block 712,determining whether the candidate corresponds to the known referencequality.

For example, a couple may be fans of a television show havingcharacteristic clever dialogue that is quickly delivered. The couplecould have been searching for similar fast-paced dead-pan deliveryprogramming and could desire to be alerted to shows that have thatdynamism and spacing. Embodiments of the invention can identify othershows that the couple is likely to enjoy.

Exemplary embodiments of the invention can aid advertisers in appealingto their target demographic. For instance, a voiceover for anadvertisement or the preeminent vocal quality for a television show canbe scored for spoken genome categories, such as gender, pitch, cadence,tonality, rhythm, accent, timing, slurring, and elocution. A variety ofdemographic groups can indicate their preference for various types ofcontent that has been scored. The preferences of such demographic groupscan enable an advertiser or producer to select or coach a narrator oractors in the content being presented according to desired attributes.

For example, a 32 year old male, who is single and living in a largecity in state X could be searching for a new car and a girlfriend.Embodiments of the invention could determine that males aged 25 to 35 instate X respond to a firm voice from a man that speaks with a steadycadence or to a woman speaking with a faster cadence and that has arelatively low-pitched voice. Embodiments of the invention could alsoidentify that in state Y, the same gender and age group respond mostfavorably to a woman with a high-pitched voice. Based at least in parton this data, car manufacturers can tailor their ads to meet theseneeds.

As another example, a couple traveling cross-country by car could be insearch of an audiobook that they are likely to enjoy. Embodiments of thepresent invention can direct the couple to audiobooks they are likely toenjoy. Or, for instance, an internet web-surfer can search the internetrepeatedly for interesting content. Embodiments of the invention can aidthe web-surfer in locating content she is more likely to find enjoyable.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, element components,and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form described. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

The flow diagrams depicted herein are just one example. There can bemany variations to this diagram or the steps (or operations) describedtherein without departing from the spirit of embodiments of theinvention. For instance, the steps can be performed in a differing orderor steps can be added, deleted or modified. All of these variations areconsidered a part of the claimed invention.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments described. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdescribed herein.

What is claimed is:
 1. A processing system for characterization andanalysis of human voice comprising: a processor in communication withone or more types of memory, the processor configured to: receive afirst reference voice sampling, wherein the first reference voicesampling comprises a plurality of reference voices corresponding to afirst known reference quality; and analyze the first voice sampling todetermine a spoken genome property corresponding to the first knownreference quality.
 2. The processing system of claim 1, wherein theprocessor is configured to output the spoken genome propertycorresponding to the first known reference quality.
 3. The processingsystem of claim 1, wherein the processor is configured to receive asecond reference voice sampling.
 4. The processing system of claim 3,wherein the second reference voice sampling comprises a plurality ofreference voices corresponding to a second known reference quality. 5.The processing system of claim 4, wherein the processor is furtherconfigured to analyze the second voice sampling to determine a spokengenome property corresponding to the second known reference quality. 6.The processing system of claim 5, wherein the processor is furtherconfigured to generate a spoken genome database.
 7. The processingsystem of claim 6, wherein the spoken genome database comprises a firstbin comprising the first known reference quality and the spoken genomeproperty corresponding to the first known reference quality.
 8. Theprocessing system of claim 7, wherein the spoken genome database furthercomprises a second bin comprising the second known reference quality andthe spoken genome property corresponding to the second known referencequality.
 9. The processing system of claim 1, wherein the processor isfurther configured to receive a target voice input corresponding to acandidate.
 10. The processing system of claim 9, wherein the processoris further configured to determine a spoken genome propertycorresponding to the target voice input.
 11. The processing system ofclaim 10, wherein the processor is further configured to compare thetarget voice input spoken genome property to the spoken genome propertycorresponding to the first known reference quality.
 12. The processingsystem of claim 11, wherein the processor is further configured todetermine whether the candidate corresponds to the first known referencequality.
 13. The processing system of claim 1, wherein the processor isconfigured to output the determination of whether the candidatecorresponds the first known reference quality to a display.
 14. Theprocessing system of claim 1, wherein the spoken genome property ispitch.
 15. The processing system of claim 1, wherein the spoken genomeproperty is cadence.
 16. The processing system of claim 1, wherein thespoken genome property is tonality.
 17. The processing system of claim1, wherein the spoken genome property is rhythm.
 18. The processingsystem of claim 1, wherein the spoken genome property is timing.
 19. Theprocessing system of claim 1, wherein the spoken genome property iselocution.
 20. The processing system of claim 1, wherein the spokengenome property is accent.